def plot(self):
import matplotlib.pyplot as plt
from common_stock.stock_plotter import StockAxisPlot
from stock_data_manager.ddr_file_cache import read_ddr_fast
df = read_ddr_fast(self.code).df
df = df[df.index <= self.date_range[1]]
df = df[df.index >= self.date_range[0]]
fig, ax = plt.subplots()
stock_plotter = StockAxisPlot((fig, ax), df, code=self.code)
from common.scipy_helper import pdSr
buy_series = pdSr(data=[item.buy_price for item in self.hold_periods],
index=[item.buy_ts for item in self.hold_periods])
sell_series = pdSr(data=[item.sell_price for item in self.hold_periods],
index=[item.sell_ts for item in self.hold_periods])
# print(buy_series)
# print(sell_series)
stock_plotter.add_scatter_point('buy', buy_series, color='k', marker='^')
stock_plotter.add_scatter_point('sell', sell_series, color='k', marker='v')
hold_days = [(item.buy_ts, item.sell_ts) for item in self.hold_periods]
stock_plotter.add_hold_info(hold_days)
plt.title(self.code)
stock_plotter.plot()
plt.show()
def main():
ddr = read_ddr_fast('510050.XSHG')
df = ddr.df.tail(6)
print(KlineFeatures.volume_percentage(df))
return
val = extract_kline_pren_feature(df, window=5)
print(val)
def analyser1(cls):
accs = SpecificRunner.emu_hs300_p2()
for trim_len in [0, 3, 5, 7]:
for acc in accs:
acc = acc.trim_hold_periods(trim_len, read_ddr_fast(acc.code).df.open)
print(acc)
acc.plot()
print()
print_line_item(cls.gain_from_acc(accs))
def test_HoldPeriod_left_trim():
hp = HoldPeriod('510050.XSHG', 20161024, 20161027, 1, 2)
assert hp
sr = read_ddr_fast('510050.XSHG').df.open
hp.left_trim(2, sr)
assert hp.buy_price == (sr.at[20161026])
assert hp
hp = hp.left_trim(1, sr)
assert not hp
hp = hp.left_trim(1, sr)
assert not hp
def main():
for trim_len in [0, 3, 5]:
import datetime
s_time = datetime.datetime.now()
accs = SpecificRunner.emu_hs_all_sv()
# accs = [item for item in accs if item.hold_yyield < 0.6]
# numpy.random.shuffle(accs)
# accs = [item for item in accs if item.code == '300099.XSHE']
# for acc in accs:
# acc = acc.trim_hold_periods(trim_len, read_ddr_fast(acc.code).df.open)
accs = [item.trim_hold_periods(trim_len, read_ddr_fast(item.code).df.open) for item in
accs]
# hold_len = []
# for val in accs:
# hold_len.extend([item.hold_len for item in val.hold_periods])
# plot_histogram(hold_len)
# print('Time: ', datetime.datetime.now() - s_time)
print('Gain:', Analyser.gain_from_acc(accs))
pass
def bs_open_open(code, time_period, day_len) -> List[SingleEmuAccount]:
df = read_ddr_fast(code).df.tail(day_len)
assert len(df) > day_len / 2, 'data len is too small'
days = df.index.values
o, c = df.open.values, df.close.values
skip_days = time_period
sma = talib.EMA(df.close.values, time_period)
index_day = list(enumerate(days))[skip_days:]
sday, eday = index_day[0], index_day[-1]
yield_ = c[eday[0]] / c[sday[0]]
acc = SingleEmuAccount(code, (sday[1], eday[1]), yield_)
for index, day in index_day:
if index <= skip_days: continue
if sma[index - 1] < c[index - 1]:
acc.buy(day, o[index])
elif sma[index - 1] > c[index - 1]:
acc.sell(day, o[index])
acc.calc_addition_infos()
return acc
def bs_open_open(code, macd_param, day_len) -> List[SingleEmuAccount]:
fast, slow, signal = macd_param
df = read_ddr_fast(code).df.tail(day_len)
if len(df) < day_len / 2: return
days = df.index.values
o, c = df.open.values, df.close.values
skip_days = slow + signal + 1
macd_vals = talib.MACD(df.close.values, fast, slow, signal)[2]
index_day = list(enumerate(days))[skip_days:]
sday, eday = index_day[0], index_day[-1]
yield_ = c[eday[0]] / c[sday[0]]
acc = SingleEmuAccount(code, (sday[1], eday[1]), yield_)
for index, day in index_day:
if index <= skip_days: continue
if macd_vals[index - 1] > 0:
acc.buy(day, o[index])
elif macd_vals[index - 1] < 0:
acc.sell(day, o[index])
acc.calc_addition_infos()
return acc
def main():
ddr = read_ddr_fast('510050.XSHG')
ddr = ddr.tail(100)
plot_df_with_min_max(ddr.df)